Image super-resolution is the task of generating a magnified high-resolution (HR) image from a low-resolution (LR) image. This magnified image should preserve the high-frequency (HF) content of the LR image. Conventionally, a HR image is obtained from a noisy LR image by first denoising and then upscaling.
WO2015121422A discloses a noise-aware single-image super-resolution (SI-SR) algorithm, which automatically cancels additive noise while adding detail learnt from lower scales. In contrast with common SI-SR techniques, the method does not assume the input image to be a clean source of examples. Instead, the method exploits a recent and efficient in-place cross-scale self-similarity prior for both learning fine detail examples to complement the interpolation-based upscaled image patches and reducing image noise.
In EP3086280, a coarse estimate of the super-resolved image is first obtained by interpolation. Then, each patch of the upscaled image is mean-subtracted and normalized to top-down traverse several independent hierarchical nonlinear mapping functions obtained during offline training. During each top-down traversal, the similarities between the hierarchical modes in the map and the normalized patch are combined and the map with best similarity is chosen, thus improving the quality achieved by an alternative solution with a single map. The mean-subtracted patch is then processed by the locally linear map corresponding to the resulting linearization in the chosen map and added to the coarse patch.
[Dabov2007] discloses image denoising by a two-step method, where each step includes a grouping of similar patches by block-matching, 3D transform, a collaborative filtering stage and an inverse 3D transform and composition of the reconstructed image. During the first stage, the collaborative filter uses a hard-thresholding, whereas during the second stage a finer Wiener denoising filter is used. The selection of the threshold and filter coefficients requires knowledge of the noise level.
[Dong2014] uses an approach based on a convolutional neural network (CNN) with multiple layers to upscale an image.